Engineering Manager, Data
Xsolla is a video game commerce company that provides a suite of tools and services—including merchant of record payment processing, tax management, fraud prevention, compliance, refunds, dispute management, and end-user support—to help game developers and publishers launch, grow, and monetize their games globally. It serves video game developers, publishers, and studios of all sizes across global and regional markets.
About Xsolla
Xsolla connects the tools, systems, payments, and web shops used by the video games industry, positioning itself as a global merchant of record supporting over 1,000 payment methods and a cumulative audience of 50 million, with transaction fees around 5%. Its services include tax management, fraud monitoring and prevention, global and regional regulatory compliance, refund and dispute management, and end-user payment support. Xsolla's product lineup includes the Xsolla SDK for native in-app payments on side-loaded apps and alternative app stores, a Buy Button enabling link-out purchases from iOS mobile games in the U.S., and Web Shop for building customized, direct-to-consumer game storefronts. The company works with major gaming industry partners and clients such as Mytona, Ubisoft, MARVEL SNAP, and others, and highlights partner success stories, industry events, and its own culture and hiring initiatives on its site.
Skills
About the Role
You will lead and grow a distributed team of data engineers, ML engineers, and data scientists delivering recommendation engines, personalization systems, and data infrastructure used by millions of players worldwide. You'll define and execute the data science and ad tech roadmap, architect ML pipelines and experimentation frameworks, and oversee real-time pipelines for ad events. You'll collaborate closely with Product, Growth, and Marketing to build audience scoring, LTV/churn models, and incrementality testing, while ensuring the data infrastructure remains scalable and privacy-compliant. You'll champion engineering excellence, reproducibility, and observability, and you'll mentor engineers and scientists to grow their technical depth and leadership skills.
Requirements
- 5+ years of experience in software/data engineering or applied data science, with 3+ years managing technical teams in ML, analytics, or ad tech domains
- Deep understanding of machine learning and statistical modeling, including regression, classification, causal inference, uplift modeling, and forecasting
- Hands-on experience with ML/data platforms such as Snowflake, BigQuery, Spark, Airflow, dbt, MLFlow, and feature stores
- Proven experience in architecting and deploying end-to-end ML systems into production (batch and real-time)
- Knowledge of ad tech ecosystems, including campaign hierarchies, attribution models (multi-touch, view-through), and creative performance tracking
- Familiarity with audience management, segmentation, and personalization frameworks in programmatic or CRM marketing
- Experience with privacy-preserving measurement, including support for SKAdNetwork, GAID/IDFA deprecation, and consent systems
- Excellent leadership, communication, and stakeholder management skills across technical and non-technical audiences
- Bachelor's or Master's in Computer Science, Engineering, Statistics, or a related field. PhD is a plus
Responsibilities
- Lead and grow a high-performing, distributed team of data scientists, ML engineers, and data platform engineers
- Define and execute the data science and ad tech roadmap, advancing initiatives in user modeling, campaign optimization, targeting, and personalization
- Architect and manage ML pipelines and experimentation frameworks, including feature engineering, training pipelines, model serving, A/B testing, and causal inference systems
- Oversee real-time pipelines for ad events, enabling responsive attribution and performance optimization
- Collaborate with Product, Growth, and Marketing to develop audience scoring, LTV/churn models, and incrementality testing for media measurement and bidding efficiency
- Ensure scalable, privacy-compliant data infrastructure aligned with GDPR, CCPA, and ATT, including support for SKAdNetwork, CMPs, and identity frameworks
- Foster engineering excellence with a focus on reproducibility, model evaluation, observability, and model lifecycle management
- Drive a strong feedback loop between experimentation and business outcomes, translating data science insights into product and go-to-market wins
- Mentor engineers and scientists on career development, technical depth, and cross-functional leadership
Benefits
- Medical, dental, and vision insurance
- PTO
- Personalized career roadmap
- Professional development through training and educational opportunities